Interview with Haimin Hu: Recreation-theoretic integration of security, interplay and studying for human-centered autonomy

On this interview collection, we’re assembly a few of the AAAI/SIGAI Doctoral Consortium contributors to seek out out extra about their analysis. On this newest interview, Haimin Hu tells us about his analysis on the algorithmic foundations of human-centered autonomy and his plans for future initiatives, and offers some recommendation for PhD college students seeking to take the following step of their profession.

Might you give us an outline of the analysis you carried out throughout your PhD?

My PhD analysis, carried out beneath the supervision of Professor Jaime Fernández Fisac within the Princeton Secure Robotics Lab, focuses on the algorithmic foundations of human-centered autonomy. By integrating dynamic recreation concept with machine studying and safety-critical management, my work goals to make sure autonomous methods, from self-driving autos to drones and quadrupedal robots, are performant, verifiable, and reliable when deployed in human-populated area. The core precept of my PhD analysis is to plan robots’ movement within the joint area of each bodily and data states, actively making certain security as they navigate unsure, altering environments and work together with people. Its key contribution is a unified algorithmic framework—backed by recreation concept—that permits robots to securely work together with their human friends, adapt to human preferences and targets, and even assist people refine their abilities. Particularly, my PhD work contributes to the next areas in human-centered autonomy and multi-agent methods:

• Reliable human–robotic interplay: Planning secure and environment friendly robotic trajectories by closing the computation loop between bodily human-robot interplay and runtime studying that reduces the robotic’s uncertainty in regards to the human.
• Verifiable neural security evaluation for advanced robotic methods: Studying sturdy neural controllers for robots with high-dimensional dynamics; guaranteeing their training-time convergence and deployment-time security.
• Scalable interactive planning beneath uncertainty: Synthesizing game-theoretic management insurance policies for advanced and unsure human–robotic methods at scale.

Was there a mission (or facet of your analysis) that was significantly fascinating?

Security in human-robot interplay is particularly troublesome to outline, as a result of it hinges on an, I’d say, nearly unanswerable query: How secure is secure sufficient when people would possibly behave in arbitrary methods? To offer a concrete instance: Is it adequate if an autonomous automobile can keep away from hitting a fallen bicycle owner 99.9% of the time? What if this price can solely be achieved by the automobile all the time stopping and ready for the human to maneuver out of the way in which?

I might argue that, for reliable deployment of robots in human-populated area, we have to complement commonplace statistical strategies with clear-cut sturdy security assurances beneath a vetted set of operation circumstances as effectively established as these of bridges, energy vegetation, and elevators. We’d like runtime studying to reduce the robotic’s efficiency loss attributable to safety-enforcing maneuvers; this requires algorithms that may scale back the robotic’s inherent uncertainty induced by its human friends, for instance, their intent (does a human driver wish to merge, minimize behind, or keep within the lane?) or response (if the robotic comes nearer, how will the human react?). We have to shut the loop between the robotic’s studying and decision-making in order that it will possibly optimize effectivity by anticipating how its ongoing interplay with the human might have an effect on the evolving uncertainty, and in the end, its long-term efficiency.

What made you wish to research AI, and the world of human-centered robotic methods specifically?

I’ve been fascinated by robotics and clever methods since childhood, after I’d spend whole days watching sci-fi anime like Cellular Go well with Gundam, Neon Genesis Evangelion, or Future GPX Cyber System. What captivated me wasn’t simply the futuristic expertise, however the imaginative and prescient of AI as a real associate—augmenting human skills slightly than changing them. Cyber System specifically planted the concept of human-AI co-evolution in my thoughts: an AI co-pilot that not solely helps a human driver navigate high-speed, high-stakes environments, but in addition adapts to the motive force’s model over time, in the end making the human a greater racer and deepening mutual belief alongside the way in which. Right this moment, throughout my collaboration with Toyota Analysis Institute (TRI), I work on human-centered robotics methods that embody this precept: designing AI methods that collaborate with folks in dynamic, safety-critical settings by quickly aligning with human intent via multimodal inputs, from bodily help to visible cues and language suggestions, bringing to life the very concepts that after lived in my childhood creativeness.

You’ve landed a college place at Johns Hopkins College (JHU) – congratulations! Might you discuss a bit in regards to the technique of job looking, and maybe share some recommendation and insights for PhD college students who could also be at the same stage of their profession?

The job search was undoubtedly intense but in addition deeply rewarding. My recommendation to PhD college students: begin pondering early in regards to the type of long-term impression you wish to make, and act early in your software bundle and job discuss. Additionally, be sure you discuss to folks, particularly your senior colleagues and friends on the job market. I personally benefited quite a bit from the next assets:

Do you’ve gotten an thought of the analysis initiatives you’ll be engaged on at JHU?

I want to assist create a future the place people can unquestionably embrace the presence of robots round them. In direction of this imaginative and prescient, my lab at JHU will examine the next matters:

• Uncertainty-aware interactive movement planning: How can robots plan secure and environment friendly movement by accounting for his or her evolving uncertainty, in addition to their potential to cut back it via future interplay, sensing, communication, and studying?
• Human–AI co-evolution and co-adaptation: How can embodied AI methods study from human teammates whereas serving to them refine present abilities and purchase new ones in a secure, customized method?
• Secure human-compatible autonomy: How can autonomous methods guarantee prescribed security whereas remaining aligned with human values and attuned to human cognitive limitations?
• Scalable and generalizable strategic decision-making: How can multi-robot methods make secure, coordinated selections in dynamic, human-populated environments?

How was the expertise attending the AAAI Doctoral Consortium?

I had the privilege of attending the 2025 AAAI Doctoral Consortium, and it was an extremely precious expertise. I’m particularly grateful to the organizers for curating such a considerate and supportive atmosphere for early-career researchers. The spotlight for me was the mentoring session with Dr Ming Yin (postdoc at Princeton, now college at Georgia Tech CSE), whose insights on navigating the unsure and aggressive job market had been each encouraging and eye-opening.

Might you inform us an fascinating (non-AI associated) reality about you?

I’m captivated with snowboarding. I discovered to ski primarily by vision-based imitation studying from a chairlift, although I’m undoubtedly paying the value now for poor generalization! Someday, I hope to construct an exoskeleton that teaches me to ski higher whereas preserving me secure on the double black diamonds.

About Haimin

Haimin Hu is an incoming Assistant Professor of Laptop Science at Johns Hopkins College, the place he’s additionally a member of the Information Science and AI Institute, the Institute for Assured Autonomy, and the Laboratory for Computational Sensing and Robotics. His analysis focuses on the algorithmic foundations of human-centered autonomy. He has obtained a number of awards and recognitions, together with a 2025 Robotics: Science and Methods Pioneer, a 2025 Cyber-Bodily Methods Rising Star, and a 2024 Human-Robotic Interplay Pioneer. Moreover, he has served as an Affiliate Editor for IEEE Robotics and Automation Letters since his fourth yr as a PhD pupil. He obtained a PhD in Electrical and Laptop Engineering from Princeton College in 2025, an MSE in Electrical Engineering from the College of Pennsylvania in 2020, and a BE in Digital and Data Engineering from ShanghaiTech College in 2018.